{
  "abstract": "Background: Esophageal squamous cell carcinoma (ESCC) accounts for over 80% of esophageal cancer cases globally. Lymph node status is the strongest determinant of survival after resection, yet occult disease is found in about 17% of patients classified as node-negative. We developed a postoperative model estimating the probability of lymph node metastasis (LNM) from the resection specimen, combining nuclear features with clinicopathological variables. Methods: Of 477 consecutive ESCC patients undergoing radical esophagectomy without neoadjuvant therapy, 416 were analyzed (LNM 217, 52.2%). Nuclear features were extracted from H&E whole-slide images (20×) by QuPath optical-density detection; LASSO logistic regression selected 17. A nomogram combining the pathomics score with age, sex, tumor location, pT category and grade was validated by 10-fold cross-validation (out-of-fold, OOF) and applied without refitting to 101 patients from another institution. Results: OOF AUC was 0.821 (95% CI 0.779–0.858) for the combined model, versus 0.791 (clinical) and 0.762 (pathomics alone). The pathomics score remained an independent predictor (adjusted OR 2.47, 95% CI 1.80–3.39; p < 0.001). LNM prevalence rose across tertiles (15.8%, 55.1%, 85.6%; p < 0.001). Among 199 node-negative patients, high pathomics risk predicted worse 3-year overall survival (77.2% vs. 94.3%; p = 0.005) and progression-free survival (75.5% vs. 88.9%; p = 0.007). Externally (n = 101; LNM 24.8%), AUCs were 0.812, 0.801 and 0.697, and adding pathomics did not significantly improve discrimination. Conclusions: Within the development cohort, pathomics added modest but significant incremental value and identified node-negative patients at elevated risk. Because the model requires the resection specimen, it cannot guide preoperative treatment selection. External validation did not confirm incremental value: the clinical component; the pathomics signature did not.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China",
    "Key Laboratory of Gastrointestinal Cancer, Ministry of Education, Fujian Medical University, Fuzhou 350001, China",
    "Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou 350001, China",
    "Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fuzhou 350001, China",
    "Clinical Research Center for Thoracic Tumors of Fujian Province, Fuzhou 350001, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Kaiming Peng"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Jieming Lu"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Peipei Zhang"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Weiguang Zhang"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Jinlong Fang"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Junhuang Lin"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Shuchen Chen"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Mingqiang Kang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vdxw28sdwdpw.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18203248",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702547/?tool=EBI",
  "id": "0vdxw28sdwdpw",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://www.mdpi.com/2072-6694/18/20/3248",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7221172256",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702547/pdf/?tool=EBI",
  "pmcid": "PMC13702547",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/0vdxw28sdwdpw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "10.3390/cancers18203248",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6694/18/20/3248"
    },
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "PMC:PMC13702547",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13702547"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2072-6694",
      "subfield": "oncology"
    }
  ],
  "title": "A Pathological Image-Based Pathomics Signature Combined with Clinicopathological Features for Predicting Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma: A Development and External Validation Study",
  "title_text": "A Pathological Image-Based Pathomics Signature Combined with Clinicopathological Features for Predicting Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma: A Development and External Validation Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:47Z",
  "url": "https://research.draconitic.com/paper/0vdxw28sdwdpw",
  "venue": "Cancers",
  "versions": []
}